Factor analysis is a powerful technique that decomposes a spectral or temporal image sequence into a small number of fundamental functions (factors) whose associated spatial distributions are called factor images. However, with factor analysis, unique solutions are not ensured. Several techniques have been developed that address this problem, in the time (dynamic studies) or energy (spectral studies) domains, to constrain the solution space and make the factor analysis approach more quantitative. Some of these techniques, in particular those based on the use of a priori physiological information, may be tailored for a particular type of clinical study. These techniques generally require modification when used in different settings. For example, a particular factor analysis approach that yields satisfactory results in studies of healthy people may not yield satisfactory results in studies of patients with different medical conditions or physiological disorders.
Some major challenges of factor analysis include improving the contrast and spatial resolution of images, estimating activity curves from noisy data, and reducing background noise contributed from emission scatter and crosstalk.